使用强化学习针对从人类偏好判断中学习到的奖励模型对策略进行微调
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模式： 三阶段训练: SFT → 奖励模型 → 结合人类偏好对齐的 PPO 优化
原因： 让 AI 行为与人类价值观对齐, 提升安全性与有用性, 减少有害输出
关键洞察： 人类偏好 > 奖励模型 > 策略优化 构成可扩展的对齐机制
Interactive RLHF training pipeline with human preferences
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Core Process: Generate multiple responses → Collect human preferences → Train reward model → Optimize policy with PPO while maintaining KL constraint.
Key Components: Supervised fine-tuning (SFT), reward modeling from preferences, proximal policy optimization (PPO) with KL penalty.
Human Feedback: Pairwise comparisons, rankings, or direct ratings that teach the model human values and preferences.
Benefits: Aligns model behavior with human values, improves helpfulness and safety, reduces harmful outputs.
通过这些精选资源加深理解
RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback (Bai et al., 2023)
Direct Preference Optimization: Your Language Model is Secretly a Reward Model (Rafailov et al., 2023)
Statistical Rejection Sampling Improves Preference Optimization (Liu et al., 2024)
Secrets of RLHF in Large Language Models (Zheng et al., 2024)